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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity

Atmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically-integrated formulation of the original one-layer DRM (Dominguez et al., 2006). By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecast regional climate model (WRF-WVT) for the North American Monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as ‘truth’), the 2L-DRM better captures the spatial extent, the net amount and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on-par with the much more complex and computationally demanding WRF-WVT model.

Dominguez, Francina↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
NASA NTRS1991

The CRAF/Cassini power subsystem - Preliminary design update

A chronology is provided of the rationale leading from the early Mariner spacecraft to the CRAF/Cassini Mariner Mark II power subsystem architecture. The display pathway began with a hybrid including a solar photovoltaic array, a radioisotope thermoelectric generator (RTG), and a battery supplying a power profile with a peak loading of about 300 W. The initial concept was to distribute power through a new solid-state, programmable switch controlled by an embedded microprocessor. As the overall mission, science, and project design matured, the power requirements increased. The design evolved from the hybrid to two RTG plus batteries to meet peak loadings of near 500 W in 1989. Later that year, circumstances led to abandonment of the distributed computer concept and a return to centralized control. Finally, as power requirements continued to grow, a third RTG was added to the design and the battery removed, with the return to the discharge-controller for transients during fault recovery procedures.

Atkins, Kenneth L.↗
DOE OSTI2021

A closer look at corrosion of steel reinforcement bars in concrete using 3D neutron and X-ray computed tomography

Corrosion of reinforcing bars constitutes the largest threat to the durability of concrete structures. Thus, several studies have investigated the nature of the corrosion products, most using post-mortem analyses. However, corrosion products evolve when in contact with oxygen, hindering result interpretation. This work presents instead a state-of-the-art, non-destructive 3D method for the assessment of corrosion of embedded reinforcements. Multimodal neutron and X-ray tomography was used to observe, non-destructively, the characteristics of the corrosion products in two concrete samples, with the aim of investigating possible benefits of the use of this technique for reinforced concrete structures. One sample was naturally corroded, extracted from an 81-year-old bridge, the other was corroded via the galvanostatic method, resulting in corrosion-induced cracks. Quantitative and qualitative data was acquired, including the iron-to-rust volumetric ratio in macroscopic interfacial voids and the thickness of the corrosion layer at the steel concrete interface. The iron-to-rust volumetric ratio corresponded to large, soluble, corrosion products, forming in environments with low availability of oxygen for both samples.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Equipping Neural Network Surrogates with Uncertainty for Propagation in Physical Systems

Coarse-grained or filtered models typically rely on closure models to account for unresolved scales. For instance, large eddy simulation for modeling turbulent fluid flows explicitly resolves the largest scales, but requires modeling closure terms to account for the sub-filter scales. With the vast amount of data available from high-fidelity simulations, there are unique opportunities to leverage data-driven modeling techniques to formulate expressive and flexible closure models. Despite their flexibility, data-driven models struggle in domain shift settings, i.e. when deployed in configurations not captured in the training dataset. In particular, the efficacy of neural network surrogates is difficult to assess a priori due to the deterministic, point-estimate nature of predictions. In high-consequence applications, such models require reliable uncertainty estimates in the data-informed and out-of-distribution regimes. To quantify uncertainties in both regimes, we employ Bayesian neural networks which are able to capture both epistemic and aleatoric uncertainties. We will discuss challenges associated with the training and evaluation of these networks. Furthermore, we will discuss uncertainty embedding strategies to enable efficient sampling and propagation of uncertainty through high-fidelity simulations.

Bayesian neural networks↗
NASA NTRS1990

Computer implementation of analysis and optimization procedures for control-structure interaction problems

Implementation aspects of control-structure interaction analysis and optimization by the staggered use of single-discipline analysis modules are discussed. The single-discipline modules include structural analysis, controller synthesis and optimization. The software modularity is maintained by employing a partitioned control-structure interaction analysis procedure, thus avoiding the need for embedding the single-discipline modules into a monolithic program. A software testbed has been constructed as a stand-alone analysis and optimization program and tested for its versatility and software modularity by applying it to the dynamic analysis and preliminary design of a prototype Earth Pointing Satellite. Experience with the in-core testbed program so far demonstrates that the testbed is efficient, preserves software modularity, and enables the analyst to choose a different set of algorithms, control strategies and design parameters via user software interfaces. Thus, the present software architecture is recommended for adoption by control-structure interaction analysts as a preliminary analysis and design tool.

Belvin, W. Keith↗
NASA NTRS2002

Response Surface Modeling of Combined-Cycle Propulsion Components using Computational Fluid Dynamics

Three examples of response surface modeling with CFD are presented for combined cycle propulsion components. The examples include a mixed-compression-inlet during hypersonic flight, a hydrogen-fueled scramjet combustor during hypersonic flight, and a ducted-rocket nozzle during all-rocket flight. Three different experimental strategies were examined, including full factorial, fractionated central-composite, and D-optimal with embedded Plackett-Burman designs. The response variables have been confined to integral data extracted from multidimensional CFD results. Careful attention to uncertainty assessment and modeling bias has been addressed. The importance of automating experimental setup and effectively communicating statistical results are emphasized.

Steffen, C. J., Jr.↗
DOE OSTI2022

Development and Validation of Resistance-Capacitance Model (RCM) for Phase Change Material (PCM) Embedded in 3D Periodic Structures

The low thermal conductivity of Phase Change Materials (PCM) can be improved with extended surfaces such as additively manufactured 3D periodic lattice structures. Three different aluminum alloy-based lattices (base sizes 10, 20, and 40 mm) with average porosity of 0.95 filled with paraffin wax, with a nominal phase change temperature of 55°C, were experimentally investigated. In this work, a computationally efficient 2D Resistance Capacitance-based model (RCM) was developed for predicting the thermal characteristics of these geometries. Non-uniform porosity in the PCM-metal domain was estimated using image processing and served as model input. The solver does not solve for higher-order physics as in CFD but still can provide a good prediction of thermal resistance and energy storage at a very low computational cost. The simulation-to-real-time factor for this geometry is of the order of 10-4, while CFD simulations typically have a real-time factor greater than 1. The model was validated against the experimental data for melting under three different heat fluxes (6250 W/m2, 12500 W/m2, and 18750 W/m2). The mean deviation of the predicted average PCM temperature was between 1.34 K-2.81 K for different cases. The maximum average temperature deviation of 5.45 K was observed for the 20 mm geometry at the highest heat flux test condition. The effects of natural convection were neglected in the model, but the predicted PCM temperature and energy storage still showed good agreement with the experimental data.

25 ENERGY STORAGE↗
DOE OSTI2025

Formation of Composite SiC/SiC Joints by Embedded Wire Chemical Vapor Deposition

The joining of ceramic monoliths or composites to date has primarily been limited to the formation of brittle monolithic joints using heterogeneous (dissimilar) materials, similar to brazing in metals. The development of a damage-tolerant joint layer by SiC fiber reinforcements is demonstrated here. Tube workpieces made of SiC fiber-SiC matrix composite are joined using a nonwoven SiC fiber mat densified by embedded wire chemical vapor deposition (EWCVD), creating a fiber-reinforced weld-like joint by homogeneous joining. EWCVD uses a localized heating method to target deposition and growth to the joint region specifically, while minimizing thermal damage to the surrounding composite tube material. X-ray computed tomography (XCT) is used to nondestructively characterize as-made joints for relative density, adhesion, and composition. In situ XCT analysis during mechanical testing revealed crack deflections in the bonding layer, which indicates a toughening mechanism typical of ceramic matrix composite phase. Gas permeation testing of these proof-of-concept composite joints identified relatively high leak rates in comparison to fully coated SiC/SiC composite tube workpieces. In conclusion, the novelty of the composite joining method and current technology challenges, including gas permeability, are discussed in comparison with traditional ceramic joints and materials.

SiC↗
DOE OSTI2024

Design, Control and Application of Next Generation Qubits

Design, Control and Application of Next Generation Qubits Arun Bansil, Northeastern University (Principal Investigator) Claudio Chamon, Boston University (Co-Investigator) Adrian Feiguin, Northeastern University (Co-Investigator) Liang Fu, MIT (Co-Investigator) Eduardo Mucciolo, Univ. of Central Florida (Co-Investigator) Qimin Yan, Temple University (Co-Investigator) The quest for developing technologies for manipulating and storing information quantum mechanically is currently led by approaches that include Josephson-junctions, ion-traps, and qubits generated by defect spins in solids. Topological qubits, however, are inherently more robust to decoherence by environmental effects, and should be able to sprint ahead once practical barriers have been overcome. At the present stage of the development of the field, it is important to explore a variety of architectures and materials beyond the conventional paradigms in order to seed breakthroughs toward building a scalable quantum computer. Our comprehensive theoretical research program involved four interconnected thrusts as follows. • A materials discovery effort in two-dimensional compounds in search of materials to support Majorana zero modes and defect structures suitable as qubits. • Exploration of architectures for topological quantum computation by investigating both superconducting Majorana qubits, and robust platforms for braiding with new “meta-materials” built of arrays of Majorana qubits. • Investigation of properties of hybrid metal-organic qubits based on transition-metal centers in graphene, and molecular crystals of polyaromatic complexes with embedded transition-metal atoms. • Development of tensor-network and semiclassical approaches to study decoherence in the presence of random and dispersive spin baths, and NV centers in diamond. The full spectrum of theoretical and numerical approaches was used to address the goals of this project including first-principles, density-matrix-renormalization group, tensor networks, and data-driven high-throughput approaches using materials database and machine-learning.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Leading-edge vortex solutions with large total pressure losses

Computations are presented for a Lambda = 75 deg delta wing in a supersonic freestream under two conditions which lead to leading-edge vortices. For one condition, analysis of the computed vortical flow reveals a closed streamline in the core. From varying computational parameters, it appears that this is due to truncation error of the convective derivatives. For the other condition, comparisons are made with wind-tunnel data, and good agreement is noted for pitot pressure distributions, flow angles on the symmetry plane, and the position of an embedded shock. Many of the aerodynamic parameters are shown to be insensitive to grid spacing.

Murman, Earll M.↗
NASA NTRS1989

Navier-Stokes solutions for flows related to store separation

The objective is developing CFD capabilities to obtain solutions for viscous flows about generic configurations of internally and externally carried stores. The emphasis is placed on the supersonic flow regime with extensions being made to the transonic regime. The project is broken into four steps: (1) Cavity flows for internal carriage configurations; (2) High angle of attack flows, which may be experienced during the separation of the stores: (3) Flows about a body near a flat plate for external carriage configurations; and (4) Flows about a body inside or in the proximity of a cavity. Three-dimensional unsteady cavity flow solutions are obtained by an explicit, MacCormack algorithm, EMCAV3, for open, close, and transitional cavities. High angle of attack flows past cylinders are solved by an implicit, upwind algorithm. All the results compare favorably with the experimental data. For flows about multiple body configurations, the Chimera embedding scheme is modified for finite-volume and multigrid algorithms, MaGGiE. Then a finite volume, implicit, upwind, multigrid Navier-Stokes solver which uses on overlapped/embedded and zonal grids, VUMXZ3, is developed from the CFL3D code. Supersonic flows past a cylinder near a flat plate are computed using this code. The results are compared with the experimental data. Currently the VUMXZ3 code is being modified to accomplish step 4 of this project. Wind tunnel experiments are also being conducted for validation purposes.

Baysal, Oktay↗
NASA NTRS2003

Probabilistic Dynamic Buckling of Smart Composite Shells

A computational simulation method is presented to evaluate the deterministic and nondeterministic dynamic buckling of smart composite shells. The combined use of composite mechanics, finite element computer codes, and probabilistic analysis enable the effective assessment of the dynamic buckling load of smart composite shells. A universal plot is generated to estimate the dynamic buckling load of composite shells at various load rates and probabilities. The shell structure is also evaluated with smart fibers embedded in the plies right below the outer plies. The results show that, on the average, the use of smart fibers improved the shell buckling resistance by about 10 percent at different probabilities and delayed the buckling occurrence time. The probabilistic sensitivities results indicate that uncertainties in the fiber volume ratio and ply thickness have major effects on the buckling load while uncertainties in the electric field strength and smart material volume fraction have moderate effects. For the specific shell considered in this evaluation, the use of smart composite material is not recommended because the shell buckling resistance can be improved by simply re-arranging the orientation of the outer plies, as shown in the dynamic buckling analysis results presented in this report.

Abumeri, Galib H.↗
NASA NTRS2007

Probabilistic Dynamic Buckling of Smart Composite Shells

A computational simulation method is presented to evaluate the deterministic and nondeterministic dynamic buckling of smart composite shells. The combined use of intraply hybrid composite mechanics, finite element computer codes, and probabilistic analysis enable the effective assessment of the dynamic buckling load of smart composite shells. A universal plot is generated to estimate the dynamic buckling load of composite shells at various load rates and probabilities. The shell structure is also evaluated with smart fibers embedded in the plies right next to the outer plies. The results show that, on the average, the use of smart fibers improved the shell buckling resistance by about 10% at different probabilities and delayed the buckling occurrence time. The probabilistic sensitivities results indicate that uncertainties in the fiber volume ratio and ply thickness have major effects on the buckling load while uncertainties in the electric field strength and smart material volume fraction have moderate effects. For the specific shell considered in this evaluation, the use of smart composite material is not recommended because the shell buckling resistance can be improved by simply re-arranging the orientation of the outer plies, as shown in the dynamic buckling analysis results presented in this report.

Chamis, Christos C.↗
NASA NTRS2008

Assuring Life in Composite Systems

A computational simulation method is presented to assure life in composite systems by using dynamic buckling of smart composite shells as an example. The combined use of composite mechanics, finite element computer codes, and probabilistic analysis enable the effective assessment of the dynamic buckling load of smart composite shells. A universal plot is generated to estimate the dynamic buckling load of composite shells at various load rates and probabilities. The shell structure is also evaluated with smart fibers embedded in the plies right below the outer plies. The results show that, on the average, the use of smart fibers improved the shell buckling resistance by about 9% at different probabilities and delayed the buckling occurrence time. The probabilistic sensitivities results indicate that uncertainties in the fiber volume ratio and ply thickness have major effects on the buckling load. The uncertainties in the electric field strength and smart material volume fraction have moderate effects and thereby in the assured life of the shell.

Chamis, Christos c.↗
NASA NTRS2011

Should Pruning be a Pre-Processor of any Linear System?

There are many real-world problems whose mathematical models turn out to be linear systems Ax = b , where A is an m by x n matrix. Each equation of the linear system is an information. An information, in a physical problem, such as 4 mangoes, 6 bananas, and 5 oranges cost $10, is mathematically modeled as 4x(sub 1) + 6x(sub 2) + 5x (sub 3) = 10, where x(sub 1), x(sub 2), x(sub 3) are each cost of one mango, that of one banana, and that of one orange, respectively. All the information put together in a specified context, constitutes the physical problem and need not be all distinct. Some of these could be redundant, which cannot be readily identified by inspection. The resulting mathematical model will thus have equations corresponding to this redundant information and hence are linearly dependent and thus superfluous. Consequently, these equations once identified should be better pruned in the process of solving the system. The benefits are (i) less computation and hence less error and consequently a better quality of solution and (ii) reduced storage requirements. In literature, the pruning concept is not in vogue so far although it is most desirable. In a numerical linear system, the system could be slightly inconsistent or inconsistent of varying degree. If the system is too inconsistent, then we should fall back on to the physical problem (PP), check the correctness of the PP derived from the material universe, modify it, if necessary, and then check the corresponding mathematical model (MM) and correct it. In nature/material universe, inconsistency is completely nonexistent. If the MM becomes inconsistent, it could be due to error introduced by the concerned measuring device and/or due to assumptions made on the PP to obtain an MM which is relatively easily solvable or simply due to human error. No measuring device can usually measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or a minimum norm solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning.

Sen, Syamal K.↗
NASA NTRS2011

Should Pruning be a Pre-Processor of any Linear System?

There are many real-world problems whose mathematical models turn out to be linear systems Ax = b, where A is an m x n matrix. Each equation of the linear system is an information. An information, in a physical problem, such as 4 mangoes, 6 bananas, and 5 oranges cost $10, is mathematically modeled as an equation 4x(sub 1) + 6x(sub 2) + 5x(sub 3) = 10 , where x(sub 1), x(sub 2), x(sub 3) are each cost of one mango, that of one banana, and that of one orange, respectively. All the information put together in a specified context, constitutes the physical problem and need not be all distinct. Some of these could be redundant, which cannot be readily identified by inspection. The resulting mathematical model will thus have equations corresponding to this redundant information and hence are linearly dependent and thus superfluous. Consequently, these equations once identified should be better pruned in the process of solving the system. The benefits are (i) less computation and hence less error and consequently a better quality of solution and (ii) reduced storage requirements. In literature, the pruning concept is not in vogue so far although it is most desirable. It is assumed that at least one information, i.e. one equation is known to be correct and which will be our first equation. In a numerical linear system, the system could be slightly inconsistent or inconsistent of varying degree. If the system is too inconsistent, then we should fall back on to the physical problem (PP), check the correctness of the PP derived from the material universe, modify it, if necessary, and then check the corresponding mathematical model (MM) and correct it. In nature/material universe, inconsistency is completely nonexistent. If the MM becomes inconsistent, it could be due to error introduced by the concerned measuring device and/or due to assumptions made on the PP to obtain an MM which is relatively easily solvable or simply due to human error. No measuring device can usually measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The . error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning.

Sen, Syamal K.↗